# Algorithm Design: A Fairness-Accuracy Frontier

* This replication package supports the paper *"Algorithm Design: A Fairness-Accuracy Frontier"* (JPE, 2024). Last updated: November 18, 2024.

* For questions or further assistance, please contact:
  - Annie Liang (liang.annie.h@gmail.com)
  - Jay Lu (jay@econ.ucla.edu)
  - Xiaosheng Mu (xmu@princeton.edu)
  - Kyohei Okumura (kyohei.okumura@gmail.com)

## Data

* Refer to `data/data_description.md` for data details.

## Notebooks

* **`notebook/obermeyer.ipynb`**  
  Contains code supporting analyses for Dataset 1, including:
  - Figure 9 (a)
  - Table 1, first column
  - Figures 10 (A) and (B)

* **`notebook/strack.ipynb`**  
  Contains code supporting analyses for Dataset 2, including:
  - Figure 9 (b)
  - Table 1, second column
  - Figures 10 (C) and (D)

## Package Requirements

To replicate our analysis, please ensure the following Python packages are installed. We recommend using [Anaconda](https://www.anaconda.com/) for package and environment management.

### Required Packages

Run the following commands to install the necessary packages:

```bash
conda install numpy pandas matplotlib seaborn scikit-learn cvxpy gurobi scipy
